In the context of exploding diversity and use of mobile devices, a fundamental research challenge is delivering efficiently and effectively the right information, to the right people, at the right time. Advertising is more productive when the recommendations are selectively channeled, taking into consideration the individual customer's real interests. Conventional systems select customers for product marketing (e.g., targeted advertisements) based on demographics and/or observed behavior from Web-based applications such as social networking sites (e.g., Facebook, twitter, etc.). The influence of friends on customer decisions has been considered, with social influence playing an important role in product marketing.
Analyzing social network data to learn about users' interests and transferring documents to users based on their profiles sketched using a database of keywords for the item recommendation have been considered. Recently, some studies consider the likes and dislikes of an individual and his or her friends' interests and works out a separate ranking module to understand the closeness of friends. Recommendation (i.e., selecting or organizing information for individual users) has been an active application area for information filtering, Web mining and machine learning research. Recent studies show that combining conceptual and usage information can improve the quality of Web recommendations.
Reinforcement learning is a new technique devised for higher quality Web recommendations based on Web usage data. Recent studies show that combining conceptual and usage information can improve the quality of Web recommendations. Reinforcement learning is a new technique devised for higher quality Web recommendations based on Web usage data.
Thus, the explosion of information is associated with an explosion of information about the users of various network applications that has the potential of being used to devise more effective recommendation methods that would better serve both product or information providers and the public.